Papaverine, a promising therapeutic agent for the treatment of COVID-19 patients with underlying cardiovascular diseases (CVDs).

The causative agent of coronavirus disease-2019 (COVID-19), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), enters the host cells via an angiotensin-converting enzyme 2 (ACE2)-mediated endocytosis-dependent manner. Because ACE2 is highly expressed in the heart, SARS-CoV-2 can severely infect heart tissue and arteries, causing acute and chronic damage to the cardiovascular system. Therefore, special attention should be paid to finding appropriate agents to protect this vital system during COVID-19 treatment. Papaverine is a unique vasodilator alkaloid that is clinically used in the treatment of vasospasm. Interestingly, this compound has potent and direct effects on a wide range of viruses, and could also prevent viral exploitation mechanisms of the host cell facilities by inhibiting some cellular signaling pathways such as p38 MAPK. This pathway was recently introduced as a promising target for the treatment of COVID-19. Papaverine also has anti-inflammatory effects which is useful in combating the hyper-inflammatory phase of the COVID-19. Unlike some medications that have severe dosage-restrictions in the treatment of COVID-19 due to cardiac side effects, papaverine is recommended for use in many heart disorders. The ability of papaverine to treat COVID-19 has become more promising when the results of some extensive screenings showed the strong ability of this compound to inhibit the cytopathic effects of SARS-CoV-2 with EC50 of 1.1 µM. Having several therapeutic effects along with desired safety profile raises this hypothesis that papaverine could be a promising compound for the suppression of SARS-CoV-2 and prevention of ischemia/vasoconstriction-related complications in COVID-19 disease, especially in patients with underlying cardiovascular diseases (CVDs).

Development of Smart Healthcare Tracker through Internet of Things

According to the World Health Organization, there are approximately 17.9 million people in the world who will die under the cause of Cardiovascular diseases (CVDs) in 2019. Heart and Brain are both related to Cardiovascular diseases. Even if the patients do not pass away due to the disease, the post-effect of this illness burdens the patients and their families. Also, the outbreak of COVID-19 makes the patients take a risk of undergoing rehabilitation in the hospital. Thus, a smart healthcare solution which is a Smart Healthcare Tracker through the Internet of Things is designed. The system consists of an EMG sensor, accelerometer, gyroscope, and heart rate/pulse oximeter connected to ESP 32 with an interface of NodeMCU to study the patients' health condition for arms and legs strength by sending the data to the caregivers or physicians. The project aimed to obtain a consistent and accurate reading for each of the features for arms and legs strength analysis and sleeping disturbance analysis. The BLYNK app is also applied to the project design as a platform to display the analysis result to the caregivers/physicians on the gadgets at any time and anywhere. The prototype has been constructed and the data collection is built successfully. The prototype is trusted to obtain accurate and consistent results and can provide a sustainable way for the rehabilitation to indicate the health condition and the recovery stage of the patients. © 2021 IEEE.

COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network.

The reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been proposed to detect the SARS-CoV-2 virus using different images and data. However, this is the first study that will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-learning techniques. A public dataset of ECG images consisting of 1937 images from five distinct categories, such as normal, COVID-19, myocardial infarction (MI), abnormal heartbeat (AHB), and recovered myocardial infarction (RMI) were used in this study. Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: (i) two-class classification (normal vs COVID-19); (ii) three-class classification (normal, COVID-19, and other CVDs), and finally, (iii) five-class classification (normal, COVID-19, MI, AHB, and RMI). For two-class and three-class classification, Densenet201 outperforms other networks with an accuracy of 99.1%, and 97.36%, respectively; while for the five-class classification, InceptionV3 outperforms others with an accuracy of 97.83%. ScoreCAM visualization confirms that the networks are learning from the relevant area of the trace images. Since the proposed method uses ECG trace images which can be captured by smartphones and are readily available facilities in low-resources countries, this study will help in faster computer-aided diagnosis of COVID-19 and other cardiac abnormalities.

Key Strategies for Clinical Management and Improvement of Healthcare Services for Cardiovascular Disease and Diabetes Patients in the Coronavirus (COVID-19) Settings: Recommendations From the REPROGRAM Consortium.

Patients with cardiovascular disease and diabetes are at potentially higher risk of infection and fatality due to COVID-19. Given the social and economic costs associated with disability due to these conditions, it is imperative that specific considerations for clinical management of these patients be observed. Moreover, the reorganization of health services around the pandemic response further exacerbates the growing crisis around limited access, treatment compliance, acute medical needs, and mental health of patients in this specific subgroup. Existing recommendations and guidelines emanating from respective bodies have addressed some of the pressure points; however, there are variations and limitations vis a vis patient with multiple comorbidities such as obesity. This article will pull together a comprehensive assessment of the association of cardiovascular disease, diabetes, obesity and COVID-19, its impact on the health systems and how best health systems can respond to mitigate current challenges and future needs. We anticipate that in the context of this pandemic, the cardiovascular disease and diabetes patients need a targeted strategy to ensure the harm to this group does not translate to huge costs to society and to the economy. Finally, we propose a triage and management protocol for patients with cardiovascular disease and diabetes in the COVID-19 settings to minimize harm to patients, health systems and healthcare workers alike.